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报告人:王聪 教授


主持人:王龙 教授

时 间:520日(周三)下午3:00

地 点:湍流实验室会议室



The Deterministic Learning Theory aims to study the largely unexplored area of knowledge acquisition, representation, and utilization in uncertain dynamic environments. Referred to as deterministic learning” in comparison with the celebrated statistical learning”, the new learning theory is developed utilizing results from concepts and tools of adaptive control and dynamical systems. Features of deterministic learning include: (i) satisfaction of a partial persistence of excitation (PE) condition due to the utilization of RBF networks, and (ii) locally-accurate identification of a partial system model associated with a periodic or periodic-like (recurrent) trajectory. The Deterministic Learning Theory provides systematic design approaches for nonlinear system identification, temporal/dynamical pattern recognition, and pattern-based control of nonlinear systems in uncertain dynamic environments.



Dr. Cong Wang received the B.E. and M.E. degrees from Beijing University of Aeronautic & Astronautics in 1989 and 1997, respectively, and the Ph.D. degree from the Department of Electrical & Computer Engineering, National University of Singapore in 2002. From 2001 to 2004, he did his postdoctoral research at the Department of Electronic Engineering, City University of Hong Kong. He has been with the College of Automation, South China University of TechnologyGuangzhou, China, since 2004, where he is currently a Professor. He has authored and co-authored over 40 international journal and conference papers. He serves as an Associate Editor of the IEEE Control Systems Society (CSS) Conference Editorial Board, and is a member of the Technical Committee on Intelligent Control of the IEEE CSS. His research interest includes deterministic learning theory, intelligent and autonomous control, dynamical pattern recognition, and intelligent diagnosis of oscillation faults.